Spatio-temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the Morecambe Bay area, England.

BACKGROUND: Promoting integrated care is a key goal of the NHS Long Term Plan to improve population respiratory health, yet there is limited data-driven evidence of its effectiveness. The Morecambe Bay Respiratory Network is an integrated care initiative operating in the North-West of England since 2017. A key target area has been reducing referrals to outpatient respiratory clinics by upskilling primary care teams. This study aims to explore space-time patterns in referrals from general practice in the Morecambe Bay area to evaluate the impact of the initiative. METHODS: Data on referrals to outpatient clinics and chronic respiratory disease patient counts between 2012-2020 were obtained from the Morecambe Bay Community Data Warehouse, a large store of routinely collected healthcare data. For analysis, the data is aggregated by year and small area geography. The methodology comprises of two parts. The first explores the issues that can arise when using routinely collected primary care data for space-time analysis and applies spatio-temporal conditional autoregressive modelling to adjust for data complexities. The second part models the rate of outpatient referral via a Poisson generalised linear mixed model that adjusts for changes in demographic factors and number of respiratory disease patients. RESULTS: The first year of the Morecambe Bay Respiratory Network was not associated with a significant difference in referral rate. However, the second and third years saw significant reductions in areas that had received intervention, with full intervention associated with a 31.8% (95% CI 17.0-43.9) and 40.5% (95% CI 27.5-50.9) decrease in referral rate in 2018 and 2019, respectively. CONCLUSIONS: Routinely collected data can be used to robustly evaluate key outcome measures of integrated care. The results demonstrate that effective integrated care has real potential to ease the burden on respiratory outpatient services by reducing the need for an onward referral. This is of great relevance given the current pressure on outpatient services globally, particularly long waiting lists following the COVID-19 pandemic and the need for more innovative models of care.

Levels of Organochlorine Pesticides Are Associated with Amyloid Aggregation in Apex Avian Brains.

Organochlorine (OC) pesticides pose a significant environmental risk to wildlife and humans and have been associated with Alzheimer's disease (AD). This study aims to spectroscopically analyze brains from free-flying birds and link the results to OC exposure and consequent amyloid aggregation. As long-lived apex predators, predatory birds represent a sentinel species similar to humans. Therefore, the results have implications for both species and may also add to our understanding of the role OC pesticides play in the development of AD. Brains of wild Sparrowhawks were analyzed using ATR-FTIR and Raman spectroscopy and Congo red staining; results were correlated with OC pesticide concentrations in livers. Effects of OC exposure were sex- and age-dependent and associated alterations were seen in lipids and protein secondary structure. A shift from α-helix to ß-sheet conformation of proteins indicated that concentrations of OC pesticides >7.18 µg/g may lead to cerebral amyloid aggregation.

Mid-infrared spectroscopic assessment of nanotoxicity in gram-negative vs. gram-positive bacteria.

Nanoparticles appear to induce toxic effects through a variety of mechanisms including generation of reactive oxygen species (ROS), physical contact with the cell membrane and indirect catalysis due to remnants from manufacture. The development and subsequent increasing usage of nanomaterials has highlighted a growing need to characterize and assess the toxicity of nanoparticles, particularly those that may have detrimental health effects such as carbon-based nanomaterials (CBNs). Due to interactions of nanoparticles with some reagents, many traditional toxicity tests are unsuitable for use with CBNs. Infrared (IR) spectroscopy is a non-destructive, high throughput technique, which is unhindered by such problems. We explored the application of IR spectroscopy to investigate the effects of CBNs on Gram-negative (Pseudomonas fluorescens) and Gram-positive (Mycobacterium vanbaalenii PYR-1) bacteria. Two types of IR spectroscopy were compared: attenuated total reflection Fourier-transform infrared (ATR-FTIR) and synchrotron radiation-based FTIR (SR-FTIR) spectroscopy. This showed that Gram-positive and Gram-negative bacteria exhibit differing alterations when exposed to CBNs. Gram-positive bacteria appear more resistant to these agents and this may be due to the protection afforded by their more sturdy cell wall. Markers of exposure also vary according to Gram status; Amide II was consistently altered in Gram-negative bacteria and carbohydrate altered in Gram-positive bacteria. ATR-FTIR and SR-FTIR spectroscopy could both be applied to extract biochemical alterations induced by each CBN that were consistent across the two bacterial species; these may represent potential biomarkers of nanoparticle-induced alterations. Vibrational spectroscopy approaches may provide a novel means of fingerprinting the effects of CBNs in target cells.

Vibrational biospectroscopy characterizes biochemical differences between cell types used for toxicological investigations and identifies alterations induced by environmental contaminants.

The use of cell-based assays is essential in reducing the number of vertebrates used in the investigation of chemical toxicities and in regulatory toxicology assessment. An important factor in obtaining meaningful results that can be accurately extrapolated is the use of biologically appropriate cell lines. In the present preliminary study, attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy with multivariate analysis was used to assess the fundamental biomolecular differences between a commonly used cell line, MCF-7 cells, and an environmentally relevant cell line derived from mallard (Anas platyrhynchos) dermal fibroblasts. To better understand differences in basic cell biochemistry, the cells were analyzed in the untreated state or post exposure to polychlorinated biphenyl (PCB) and polybrominated diphenyl ester (PBDE) congeners. The main spectral peaks in spectra from both cell types were associated with cellular macromolecules, particularly proteins and lipids, but the spectra also revealed some cell-specific differences. Spectra from untreated mallard fibroblasts spectra contained a large peak associated with lipids. The cell-related differences in lipids and deoxyribonucleic acid (DNA) were also identified as regions of spectral alteration induced by PBDE and PCB exposure. Although lipid alterations were observed in post treatment spectra from both cell types, these may be of more significance to mallard fibroblasts, which may be the result of increased intracellular lipid as determined by Nile red staining. Untreated MCF-7 cell spectra contained unique peaks related to DNA and nucleic acids. The DNA-associated spectral regions were also identified as areas of considerable alteration in MCF-7 cells exposed to some congeners including PBDE 47 and PCB 153. The findings indicate that in their native state, MCF-7 and mallard cells have unique biochemical differences, which can be identified using ATR-FTIR spectroscopy. Such differences in biochemical composition may influence cell susceptibility to environmental contaminants and therefore influence the choice of cell type used in toxicology experiments. To our knowledge, the present study is the first study to analyze the biochemistry of a mallard dermal fibroblast cell line and to use ATR-FTIR spectroscopy for this purpose. Thus ATR-FTIR spectroscopy is demonstrated to be a useful tool for exploration of biomolecular variation at the cellular level and with further development, it could be used as part of a panel of cell-based assays to indicate when different results might be seen in environmental species compared with currently used cell lines. Environ Toxicol Chem 2017;36:3127-3137. © 2017 SETAC.

An imaging dataset of cervical cells using scanning near-field optical microscopy coupled to an infrared free electron laser.

Using a scanning near-field optical microscope coupled to an infrared free electron laser (SNOM-IR-FEL) in low-resolution transmission mode, we collected chemical data from whole cervical cells obtained from 5 pre-menopausal, non-pregnant women of reproductive age, and cytologically classified as normal or with different grades of cervical cell dyskaryosis. Imaging data are complemented by demography. All samples were collected before any treatment. Spectra were also collected using attenuated total reflection, Fourier-transform (ATR-FTIR) spectroscopy, to investigate the differences between the two techniques. Results of this pilot study suggests SNOM-IR-FEL may be able to distinguish cervical abnormalities based upon changes in the chemical profiles for each grade of dyskaryosis at designated wavelengths associated with DNA, Amide I/II, and lipids. The novel data sets are the first collected using SNOM-IR-FEL in transmission mode at the ALICE facility (UK), and obtained using whole cells as opposed to tissue sections, thus providing an 'intact' chemical profile. These data sets are suited to complementing future work on image analysis, and/or applying the newly developed algorithm to other datasets collected using the SNOM-IR-FEL approach.

Imaging cervical cytology with scanning near-field optical microscopy (SNOM) coupled with an IR-FEL.

Cervical cancer remains a major cause of morbidity and mortality among women, especially in the developing world. Increased synthesis of proteins, lipids and nucleic acids is a pre-condition for the rapid proliferation of cancer cells. We show that scanning near-field optical microscopy, in combination with an infrared free electron laser (SNOM-IR-FEL), is able to distinguish between normal and squamous low-grade and high-grade dyskaryosis, and between normal and mixed squamous/glandular pre-invasive and adenocarcinoma cervical lesions, at designated wavelengths associated with DNA, Amide I/II and lipids. These findings evidence the promise of the SNOM-IR-FEL technique in obtaining chemical information relevant to the detection of cervical cell abnormalities and cancer diagnosis at spatial resolutions below the diffraction limit (≥0.2 µm). We compare these results with analyses following attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy; although this latter approach has been demonstrated to detect underlying cervical atypia missed by conventional cytology, it is limited by a spatial resolution of ~3 µm to 30 µm due to the optical diffraction limit.

Using Fourier transform IR spectroscopy to analyze biological materials.

IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.